QC and filter seurat object

# Seurat 
library(Seurat)

# Single R
library(SingleCellExperiment)
library(SingleR)
library(celldex)

# Data 
library(dplyr)
library(Matrix)

# Plotting
library(ggplot2)
library(RColorBrewer)
library(patchwork)

Attaching SeuratObject

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Welcome to Bioconductor

    Vignettes contain introductory material; view with
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# Set working directory to project root
setwd("/research/peer/fdeckert/FD20200109SPLENO")

# Source files
source("plotting_global.R")

Parameter settings

# Filtering Parameter
nFeature_RNA_min_m <- 500
nFeature_RNA_min_p <- 500

nFeature_RNA_max_m <- 3000
nFeature_RNA_max_p <- 3000

nCount_RNA_min_m <- 1500
nCount_RNA_min_p <- 1500

nCount_RNA_max_m <- 20000
nCount_RNA_max_p <- 20000

pMt_RNA_max_m <- 7.5
pMt_RNA_max_p <- 7.5

# Files 
so_raw_file <- "data/object/raw.rds"
so_qc_file <- "data/object/seurat.rds"

# Plotting Theme
ggplot2::theme_set(theme_global_set()) # From project global source()

Import Seurat object

so_raw <- readRDS(so_raw_file)

Add filter meta data to Seurat object

so_raw$nFeature_RNA_max <- ifelse(so_raw$tissue == "Myeloid", nFeature_RNA_max_m, nFeature_RNA_max_p)
so_raw$nFeature_RNA_min <- ifelse(so_raw$tissue == "Myeloid", nFeature_RNA_min_m, nFeature_RNA_min_p)

so_raw$nCount_RNA_max <- ifelse(so_raw$tissue == "Myeloid", nCount_RNA_max_m, nCount_RNA_max_p)
so_raw$nCount_RNA_min <- ifelse(so_raw$tissue == "Myeloid", nCount_RNA_min_m, nCount_RNA_min_p)

so_raw$pMt_RNA_max <- ifelse(so_raw$tissue == "Myeloid", pMt_RNA_max_m, pMt_RNA_max_p)

# QC 
so_raw$qc_class <- ifelse(
  so_raw$cellranger_class == "Cell" &
    so_raw$nFeature_RNA <= so_raw$nFeature_RNA_max & 
    so_raw$nFeature_RNA >= so_raw$nFeature_RNA_min & 
    so_raw$nCount_RNA <= so_raw$nCount_RNA_max & 
    so_raw$nCount_RNA >= so_raw$nCount_RNA_min &
    so_raw$pMt_RNA <= so_raw$pMt_RNA_max, 
  "pass", "fail"
  )

Filter cells by rank plot

Empty droplets were determined with CellRanger V3.0.2 Lun et al., 2019 EmptyDrop heuristic. RNAse activity of granulocytes might be wrongly identified as empty cells by CellRanger.

Typical Sample A steep drop-off is indicative of good separation between the cell-associated barcodes and the barcodes associated with empty GEMs. A ideal barcode rank plot has a distincitve shape, which is referred to as a "cliff and knee".

Heterogeneous Sample Heterogeneous populations of cells in a sample result in two "cliff and knee" distributions. However, there should still be clear separation between the bacodes.

Compromised Sample Round curve and lack of steep cliff may indicate low sample quality or loss of single-cell behavior. This can be due to a wetting failure, premature cell lysis, or low cell viability.

Compromised Sample Defined cliff and knee, but the total number of barcodes detected may be lower than expected. This can be caused by a sample clog or inaccurate cell count.

rank_plot <- ggplot(so_raw@meta.data, aes(x = log10(nCount_RNA_rank), y = log10(nCount_RNA), color = cellranger_class)) + 
  geom_point() + 
  geom_hline(aes(yintercept = log10(nCount_RNA_min)), color = "red", linetype = "longdash") +
  scale_color_manual(values = color$cellranger_class) +
  ggtitle("Barcode rank plot") +
  xlab("log10(cell barcode rank)") + ylab("log10(cell UMI counts)") + 
  facet_grid(tissue~treatment) + 
  theme(aspect.ratio = 1, legend.position = "bottom")

options(repr.plot.width = 5, repr.plot.height = 5)
rank_plot
ggsave(rank_plot, filename = "result/plot/seurat/rank_plot.png", width = 4, height = 4)

Filter by cell_ranger class

so_qc <- subset(so_raw, subset = cellranger_class == "Cell")

so_qc$sample_rep <- gsub("^.*_", "", so_qc$sample_name)

Filter by UMI and Feature count

qc_1 <- ggplot(so_qc@meta.data, aes(x = log10(nCount_RNA), fill = tissue)) + 
  geom_density() + 
  ggtitle("Density plot UMI count") + xlab("log10(UMI count)") + ylab("Density") +
  geom_vline(aes(xintercept = log10(nCount_RNA_min)), color = "red", linetype = "longdash") +
  geom_vline(aes(xintercept = log10(nCount_RNA_max)), color = "red", linetype = "longdash") + 
  scale_x_continuous(breaks = integer_breaks()) + 
  scale_fill_manual(values = color$tissue) +
  facet_grid(tissue~treatment+sample_rep) + 
  theme(legend.position = "bottom", aspect.ratio = 1) 

qc_2 <- ggplot(so_qc@meta.data, aes(x = log10(nFeature_RNA), fill = tissue)) + 
  geom_density() + 
  ggtitle("Density plot Feature count") + xlab("log10(Feature count)") + ylab("Density") +
  geom_vline(aes(xintercept = log10(nFeature_RNA_min)), color = "red", linetype = "longdash") +
  geom_vline(aes(xintercept = log10(nFeature_RNA_max)), color = "red", linetype = "longdash") + 
  scale_x_continuous(breaks = integer_breaks()) + 
  scale_fill_manual(values = color$tissue) +
  facet_grid(tissue~treatment+sample_rep) + 
  theme(legend.position = "bottom", aspect.ratio = 1)

qc_3 <- ggplot(so_qc@meta.data, aes(x = pMt_RNA, fill = tissue)) + 
  geom_density() + 
  ggtitle("Density plot Mt %") + xlab("Mt [%]") + ylab("Density") +
  geom_vline(aes(xintercept = pMt_RNA_max), color = "red", linetype = "longdash") +
  scale_x_continuous(breaks = integer_breaks()) +
  scale_fill_manual(values = color$tissue) +
  facet_grid(tissue~treatment+sample_rep)  + 
  theme(legend.position = "bottom", aspect.ratio = 1)

options(repr.plot.width = 15, repr.plot.height = 10)
qc_1 + qc_2 + qc_3 + plot_layout(ncol = 2) & theme(legend.position = "bottom")
ggsave(qc_1, filename = "result/plot/seurat/density_umi.png", width = 4, height = 4)
ggsave(qc_2, filename = "result/plot/seurat/density_feature.png", width = 4, height = 4)
ggsave(qc_3, filename = "result/plot/seurat/density_mt.png", width = 4, height = 4)

sc_1 <- ggplot(so_qc@meta.data, aes(x = log10(nCount_RNA), y = log10(nFeature_RNA), color = pMt_RNA)) + 
  geom_point() + ggtitle("Mitochondrial gene percentage") + ylab("log10(feature count)") + xlab("log10(umi count)") + 
  geom_vline(aes(xintercept = log10(nCount_RNA_min)), color = "red", linetype = "longdash") +
  geom_vline(aes(xintercept = log10(nCount_RNA_max)), color = "red", linetype = "longdash") +
  geom_hline(aes(yintercept = log10(nFeature_RNA_min)), color = "red", linetype = "longdash") + 
  geom_hline(aes(yintercept = log10(nFeature_RNA_max)), color = "red", linetype = "longdash") + 
  facet_grid(tissue~treatment+sample_rep) + theme(aspect.ratio = 1, legend.position = "bottom") + 
  scale_size(guide = guide_legend(direction = "vertical"))

sc_2 <- ggplot(so_qc@meta.data, aes(x = log10(nCount_RNA), y = log10(nFeature_RNA), color = pHb_RNA)) + 
  geom_point() + ggtitle("Hemoglobin gene percentage") + ylab("log10(feature count)") + xlab("log10(umi count)") +
  geom_vline(aes(xintercept = log10(nCount_RNA_min)), color = "red", linetype = "longdash") +
  geom_vline(aes(xintercept = log10(nCount_RNA_max)), color = "red", linetype = "longdash") +
  geom_hline(aes(yintercept = log10(nFeature_RNA_min)), color = "red", linetype = "longdash") + 
  geom_hline(aes(yintercept = log10(nFeature_RNA_max)), color = "red", linetype = "longdash") + 
  facet_grid(tissue~treatment+sample_rep) + theme(aspect.ratio = 1, legend.position = "bottom") + 
  scale_size(guide = guide_legend(direction = "vertical"))

sc_3 <- ggplot(so_qc@meta.data, aes(x = log10(nCount_RNA), y = log10(nFeature_RNA), color = pRp_RNA)) + 
  geom_point() + ggtitle("Ribsonmal gene percentage") + ylab("log10(feature count)") + xlab("log10(umi count)") +
  geom_vline(aes(xintercept = log10(nCount_RNA_min)), color = "red", linetype = "longdash") +
  geom_vline(aes(xintercept = log10(nCount_RNA_max)), color = "red", linetype = "longdash") +
  geom_hline(aes(yintercept = log10(nFeature_RNA_min)), color = "red", linetype = "longdash") + 
  geom_hline(aes(yintercept = log10(nFeature_RNA_max)), color = "red", linetype = "longdash") + 
  facet_grid(tissue~treatment+sample_rep) + theme(aspect.ratio = 1, legend.position = "bottom") + 
  scale_size(guide = guide_legend(direction = "vertical"))

options(repr.plot.width = 15, repr.plot.height = 10)
sc_1 + sc_2 + sc_3 + plot_layout(ncol = 2) & theme(legend.position = "bottom")
ggsave(sc_1, filename = "result/plot/seurat/sc_mt.png", width = 4, height = 4)
ggsave(sc_2, filename = "result/plot/seurat/sc_hg.png", width = 4, height = 4)
ggsave(sc_3, filename = "result/plot/seurat/sc_rb.png", width = 4, height = 4)

so_qc <- subset(so_qc, subset = nFeature_RNA >= nFeature_RNA_min & nFeature_RNA <= nFeature_RNA_max & nCount_RNA >= nCount_RNA_min & nCount_RNA <= nCount_RNA_max & pMt_RNA <= pMt_RNA_max)

Cell Cycle Raw Seurat Object

cc_kowalczyk <- read.csv("cc_kowalczyk.csv")
cc_kowalczyk <- cc_kowalczyk[cc_kowalczyk$sig_population >= 5, ]

cc_kowalczyk <- cc_kowalczyk[cc_kowalczyk$gene %in% rownames(so_raw), ]

cc_kowalczyk_g0 <- cc_kowalczyk[cc_kowalczyk$state == "down", ]
cc_kowalczyk_int <- cc_kowalczyk[cc_kowalczyk$state == "up", ]

cc_kowalczyk_s <- cc_kowalczyk_int[cc_kowalczyk_int$S > cc_kowalczyk_int$G2_M, ]
cc_kowalczyk_g2m <- cc_kowalczyk_int[cc_kowalczyk_int$G2_M > cc_kowalczyk_int$S, ]

so_raw <- CellCycleScoring(so_raw, s.features = cc_kowalczyk_s$gene, g2m.features = cc_kowalczyk_g2m$gene, set.ident = FALSE)
colnames(so_raw@meta.data) <- gsub("Phase", "cc_phase_class", colnames(so_raw@meta.data))
colnames(so_raw@meta.data) <- gsub("S.Score", "msS_RNA", colnames(so_raw@meta.data))
colnames(so_raw@meta.data) <- gsub("G2M.Score", "msInt_RNA", colnames(so_raw@meta.data)) 
so_raw$msCC_diff_RNA <- so_raw$msS_RNA - so_raw$msInt_RNA

so_raw <- AddModuleScore(so_raw, features = list(cc_kowalczyk_g0$gene), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msG0_RNA")
so_raw <- AddModuleScore(so_raw, features = list(cc_kowalczyk_int$gene), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msInt_RNA")

Box plots

qc_vln_FUN <- function(data, y, fill, ylab = "", ymin, ymax) {
  
  vln_plot_1 <- ggplot(data, aes(x = treatment, y = {{y}}, color = {{fill}})) + 
    geom_jitter(alpha = 0.2, shape = 16, color = "gray") + 
    geom_boxplot(alpha = 1.0) + xlab("") + 
    ylim(ymin, ymax) +
    scale_color_manual(values = color$tissue) + 
    ggtitle("Cell containing GEM") + 
    facet_wrap(~tissue, scales = "free_x") + ylab(ylab) +
    theme(
      plot.title = element_text(size = 12, face = "bold", margin = margin(t = 0, r = 0, b = 5, l = 0)), 
      axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1), 
      strip.text = element_blank()
      )
  
  vln_plot_2 <- ggplot(data[data$qc_class == "pass", ], aes(x = treatment, y = {{y}}, color = {{fill}})) +
    geom_jitter(alpha = 0.2, shape = 16, color = "gray") +
    geom_boxplot(alpha = 1.0) + xlab("") + 
    ylim(ymin, ymax) +
    scale_color_manual(values = color$tissue) +
    ggtitle("Filtered") +
    facet_wrap(~tissue, scales = "free_x") + ylab(ylab) + 
    theme(
      plot.title = element_text(size = 12, face = "bold", margin = margin(t = 0, r = 0, b = 5, l = 0)), 
      axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1), 
      strip.text = element_blank()
      )

  vln_plot <- vln_plot_1 + vln_plot_2 + plot_layout(guides = "collect") & theme(legend.position = "bottom")

  return(vln_plot)

  
}

data <- dplyr::filter(so_raw@meta.data, cellranger_class == "Cell")

qc_vln_1 <- qc_vln_FUN(data, nCount_RNA, tissue, "UMI [count]",  ymin = 0, ymax = max(so_raw$nCount_RNA)+100)
qc_vln_2 <- qc_vln_FUN(data, nFeature_RNA, tissue, "Feature [count]", ymin = 0, ymax = max(so_raw$nFeature_RNA)+100)
qc_vln_3 <- qc_vln_FUN(data, msCC_diff_RNA, tissue, "cc diff [count]", ymin = min(so_raw$msCC_diff_RNA), ymax = max(so_raw$msCC_diff_RNA))
qc_vln_4 <- qc_vln_FUN(data, pMt_RNA, tissue, "Mt [%]", ymin = 0, ymax = 100)
qc_vln_5 <- qc_vln_FUN(data, pHb_RNA, tissue, "Hb [%]", ymin = 0, ymax = 100)
qc_vln_6 <- qc_vln_FUN(data, pRp_RNA, tissue, "Rbl [%]", ymin = 0, ymax = 100)

options(repr.plot.width = 10, repr.plot.height = 5)
qc_vln_1
qc_vln_2
qc_vln_3
qc_vln_4
qc_vln_5
qc_vln_6

ggsave(qc_vln_1, filename = "result/plot/seurat/qc_vln_1.png", width = 5, height = 2.5)
ggsave(qc_vln_2, filename = "result/plot/seurat/qc_vln_2.png", width = 5, height = 2.5)
ggsave(qc_vln_3, filename = "result/plot/seurat/qc_vln_3.png", width = 5, height = 2.5)
ggsave(qc_vln_4, filename = "result/plot/seurat/qc_vln_4.png", width = 5, height = 2.5)
ggsave(qc_vln_5, filename = "result/plot/seurat/qc_vln_5.png", width = 5, height = 2.5)
ggsave(qc_vln_6, filename = "result/plot/seurat/qc_vln_6.png", width = 5, height = 2.5)

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Cell cycle QC Seurat object

so_qc <- CellCycleScoring(so_qc, s.features = cc_kowalczyk_s$gene, g2m.features = cc_kowalczyk_g2m$gene, set.ident = FALSE)
colnames(so_qc@meta.data) <- gsub("Phase", "cc_phase_class", colnames(so_qc@meta.data))
colnames(so_qc@meta.data) <- gsub("S.Score", "msS_RNA", colnames(so_qc@meta.data))
colnames(so_qc@meta.data) <- gsub("G2M.Score", "msInt_RNA", colnames(so_qc@meta.data)) 
so_qc$msCC_diff_RNA <- so_qc$msS_RNA - so_qc$msInt_RNA

so_qc <- AddModuleScore(so_qc, features = list(cc_kowalczyk_g0$gene), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msG0_RNA")
so_qc <- AddModuleScore(so_qc, features = list(cc_kowalczyk_int$gene), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msInt_RNA")

Normalize

so_qc <- NormalizeData(
  object               = so_qc, 
  assay                = "RNA", 
  normalization.method = "LogNormalize", 
  scale.factor         = 10000
  )

Variable feature selection

so_qc <- FindVariableFeatures(
  object           = so_qc, 
  assay            = "RNA", 
  selection.method = "vst", 
  nfeatures        = 3000
  )

Remove cell cycle variable feature

variable_features <- VariableFeatures(so_qc)
VariableFeatures(so_qc) <- variable_features[!variable_features %in% c(cc_kowalczyk$gene)]

Scale data

Regress nCount_RNA (total UMI per cell) and pMT_RNA (mitochondrial UMI percentage)

so_qc <- ScaleData(
  object          = so_qc, 
  assay           = "RNA", 
  vars.to.regress = c("nCount_RNA", "pMt_RNA", "msCC_diff_RNA"), 
  do.scale        = TRUE, 
  do.center       = TRUE
  )

Regressing out nCount_RNA, pMt_RNA, msCC_diff_RNA

Centering and scaling data matrix

Dim reduction

source("bin/seurat_function.R")
so_qc <- dim_clust(
    # Seurat 
    so = so_qc, 
    assay = "RNA",
    # PCA features
    blacklist_genes = NULL, 
    # Dim reduction
    dims_pca  = 100,  # Default 20 - RunPCA dims
    dims_umap = 90,  # Default NULL - RunUMAP dims 
    dims_tsne = 30,  # Default 1:5
    min_dist  = 0.3,  # Default 0.3  - RunUMAP dmin.ist - controls how tightly the embedding
    # Cluster 
    dims_cluster = 10,  # Default 1:10 - FindNeighbors dims  
    cluster_res  = 0.8 # Default 0.8 - FindClusters resoluton - above(below) 1.0 to obtain larger(smaller) number of communities)
)

PC_ 1 
Positive:  Car2, Prdx2, Blvrb, Rps2, Glrx5, Hbb-bs, Hba-a1, Hbb-bt, Cd24a, Car1 
	   Ctse, Cpox, Hba-a2, Rhd, Ybx3, Hsp90aa1, Mgst3, Eif5a, Odc1, C1qtnf12 
	   Hdgf, Ermap, Hemgn, Cldn13, Gypa, Ncl, Stard10, Klf1, Rps17, Ubac1 
Negative:  Tyrobp, Tmsb4x, Ptprc, Laptm5, Cd52, Fcer1g, Ctss, Cd74, Cyba, Coro1a 
	   Lcp1, Lsp1, Cst3, Sh3bgrl3, Napsa, Gm2a, Rgs2, Lst1, Psap, Sat1 
	   Spi1, Arpc1b, Gngt2, H2-Aa, Fyb, Pld4, Pou2f2, Cybb, H2-Eb1, H2-Ab1 
PC_ 2 
Positive:  Ciita, Dnase1l3, H2-Eb1, H2-Ab1, Ccnd1, H2-Aa, H2-Oa, S100a11, Slamf7, Wdfy4 
	   Tbc1d4, Xcr1, H2-DMa, Jaml, Batf3, H2-DMb2, Ptms, Flt3, Ppp1r14a, Adam23 
	   Ffar2, Cd8a, Cd74, Avpi1, Siglecg, H2-Ob, Rab7b, Clec9a, H2-DMb1, Gsn 
Negative:  Csf1r, Cebpb, Adgre4, Ace, Ctsb, Lrp1, Fcgr4, Pilra, Clec4a3, Lyz2 
	   Msrb1, Hip1, Cd300e, Cd300ld, Cx3cr1, Fabp4, Hpgd, Smpdl3a, Cyp4f18, Pou2f2 
	   Ceacam1, Itgal, Tnfrsf1b, Pilrb2, Stk10, Treml4, Cybb, Nupr1, Tppp3, Smpdl3b 
PC_ 3 
Positive:  Ace, Itgal, Pglyrp1, Spn, Lyz2, Cyp4f18, Eno3, Plac8, Cx3cr1, Msrb1 
	   Ceacam1, Ifitm2, Klf4, Klf2, Nr4a1, Emp3, Cebpb, Ifitm6, Tppp3, Coro1a 
	   Smpdl3b, Hp, Nupr1, Il17ra, Stap1, Rbpms, Cd300ld, Ifitm3, Ear2, Myo1g 
Negative:  Vcam1, C1qc, C1qa, C1qb, Fcna, Cd5l, Mertk, Igf1, Mrc1, Axl 
	   Maf, Itgad, Slc40a1, Mafb, Spic, Ccr3, Hmox1, Frmd4b, Ptgs1, Epb41l3 
	   Slpi, Kcna2, Kcnj10, Hpgds, Sema6d, Gfra2, Sdc3, Dst, Pld3, Clec4n 
PC_ 4 
Positive:  Fth1, Hbb-bt, Hba-a1, Hba-a2, Hbb-bs, Ctse, Cd24a, Gypa, Mgst3, Rhd 
	   Cldn13, Ftl1, Slc4a1, Hebp1, Hemgn, Cpox, Tspan33, Tfrc, Ubac1, Hagh 
	   Smim1, Prdx2, Isg20, Glrx5, Blvrb, Slc25a37, Car2, Carhsp1, Rhag, Lmna 
Negative:  Cmtm7, S100a10, Myb, Ptprcap, Gata2, Tagln2, Sox4, Cd63, Gm15915, Cpa3 
	   Lgals1, Apoe, Ctsg, Ifitm1, Ctla2a, Ms4a2, Vim, Serpinb1a, Srgn, Phgdh 
	   Angpt1, Ifitm2, Mcpt8, Muc13, Ikzf2, Fcer1a, Gmfg, Plac8, Ms4a3, Cst7 
PC_ 5 
Positive:  Ccl5, S100a4, Ccnd1, Relb, Ffar2, Siglecg, Dtx1, Ltb, Tbc1d4, Tmem176b 
	   Cd7, H2-DMb2, Il4i1, Plxnc1, Tmem176a, Ppp1r14a, Cyp4f16, Rgs1, Traf1, Lyst 
	   Ryr3, H2-Eb2, Arap2, St8sia6, Avpi1, Tctex1d2, Man1a, Icosl, Ube2e2, Adam23 
Negative:  Xcr1, Naaa, Rab7b, Irf8, Cd8a, Clec9a, Ppt1, Tlr3, Cadm1, A530099J19Rik 
	   P2ry14, Mpeg1, AC163354.1, Ifi205, Pianp, Itgae, Pdia5, Gcsam, Lgals3, Olfm1 
	   Sept3, Cst3, Aif1, Tlr11, Dbn1, Crip1, Btla, Cxcl9, Slc8b1, Ece1 

Computing nearest neighbor graph

Computing SNN

Computing nearest neighbors

Only one graph name supplied, storing nearest-neighbor graph only

Warning message:
“The following arguments are not used: assay”
Warning message:
“The following arguments are not used: assay”

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 22201
Number of edges: 695360

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8892
Number of communities: 22
Elapsed time: 3 seconds

Warning message:
“The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session”
14:15:25 UMAP embedding parameters a = 0.9922 b = 1.112

14:15:26 Commencing smooth kNN distance calibration using 1 thread

14:15:29 Initializing from normalized Laplacian + noise

14:15:31 Commencing optimization for 200 epochs, with 565500 positive edges

14:15:54 Optimization finished

14:15:54 UMAP embedding parameters a = 0.9922 b = 1.112

14:15:54 Read 22201 rows and found 90 numeric columns

14:15:54 Using Annoy for neighbor search, n_neighbors = 20

14:15:54 Building Annoy index with metric = euclidean, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

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14:15:57 Writing NN index file to temp file /tmp/Rtmp9uQVAH/file226c1df1d2d6

14:15:57 Searching Annoy index using 1 thread, search_k = 2000

14:16:02 Annoy recall = 100%

14:16:04 Commencing smooth kNN distance calibration using 1 thread

14:16:07 Initializing from normalized Laplacian + noise

14:16:08 Commencing optimization for 200 epochs, with 687332 positive edges

14:16:34 Optimization finished

Warning message:
“Cannot add objects with duplicate keys (offending key: UMAP_), setting key to 'rna_umap_'”

SingleR annotation

SingleR identifies marker genes from the reference and uses them to compute assignment scores (based on the Spearman correlation across markers) for each cell in the test dataset against each label in the reference. The label with the highest score is the assigned to the test cell, possibly with further fine-tuning to resolve closely related labels.

first.labels: Labels before fine-tuning
labels: Labels after fine-tuning
pruning: labels after pruning

ref <- readRDS("data/haemosphere/se_haemosphere.rds")

Annotate all cells

# Seurat to SingleCellExperiment 
sce <- SingleCellExperiment(list(counts = so_qc@assays$RNA@counts))

# Predict labels
label_main <- SingleR::SingleR(test = sce, ref = ref, labels = ref$label.main, assay.type.test = "counts", de.method = "classic")
label_fine <- SingleR::SingleR(test = sce, ref = ref, labels = ref$label.fine, assay.type.test = "counts", de.method = "classic")

# Add labels to Seurat object
label_main_meta <- as.data.frame(label_main) %>% dplyr::select(pruned.labels, tuning.scores.first) %>% dplyr::rename(main_labels = pruned.labels, main_delta_score = tuning.scores.first)
label_fine_meta <- as.data.frame(label_fine) %>% dplyr::select(pruned.labels, tuning.scores.first) %>% dplyr::rename(fine_labels = pruned.labels, fine_delta_score = tuning.scores.first)

so_qc <- AddMetaData(so_qc, cbind(label_main_meta, label_fine_meta))

# Set factor level for labels 
so_qc$main_labels <- factor(so_qc$main_labels, levels = names(color$main_labels_haemosphere))
so_qc$fine_labels <- factor(so_qc$fine_labels, levels = names(color$fine_labels_haemosphere))

Dim and Feature plots

reduction <- "rna_umap_nno"

dplot_1 <- DimPlot(so_qc, reduction = reduction, group.by = "rna_snn_res.0.8", label = TRUE) & 
    theme(aspect.ratio = 1, legend.position = "none")
dplot_2 <- DimPlot(so_qc, reduction = reduction, group.by = "main_labels", label = FALSE) & 
    theme(aspect.ratio = 1, legend.position = "bottom") & 
    scale_color_manual(values = color$main_labels, na.value = "dark gray") & 
    guides(color = guide_legend(ncol = 3, override.aes = list(size = 2)))
dplot_3 <- DimPlot(so_qc, reduction = reduction, group.by = "fine_labels", label = FALSE) & 
    theme(aspect.ratio = 1, legend.position = "bottom") & 
    scale_color_manual(values = color$fine_labels, na.value = "dark gray") & 
    guides(color = guide_legend(ncol = 3, override.aes = list(size = 2)))
dplot_4 <- DimPlot(so_qc, reduction = reduction, group.by = "tissue", label = FALSE) & 
    theme(aspect.ratio = 1, legend.position = "bottom") & 
    scale_color_manual(values = color$tissue, na.value = "dark gray") & 
    guides(color = guide_legend(ncol = 3, override.aes = list(size = 2)))
dplot_5 <- DimPlot(so_qc, reduction = reduction, group.by = "treatment", label = FALSE) & 
    theme(aspect.ratio = 1, legend.position = "bottom") & 
    scale_color_manual(values = color$treatment, na.value = "dark gray") & 
    guides(color = guide_legend(ncol = 3, override.aes = list(size = 2)))

options(repr.plot.width = 15, repr.plot.height = 10)
dplot <- dplot_1 + dplot_2 + dplot_3 + dplot_4 + dplot_5 + plot_layout(ncol = 3)
dplot
ggsave(dplot, filename = "result/plot/seurat/dimplot_1.png", width = 15, height = 10)

fplot_1 <- FeaturePlot(so_qc, reduction = reduction, features = "nCount_RNA") & theme(aspect.ratio = 1)
fplot_2 <- FeaturePlot(so_qc, reduction = reduction, features = "nFeature_RNA") & theme(aspect.ratio = 1)
fplot_3 <- FeaturePlot(so_qc, reduction = reduction, features = "main_delta_score") & theme(aspect.ratio = 1)

options(repr.plot.width = 15, repr.plot.height = 5)
fplot <- fplot_1 + fplot_2 + fplot_3 + plot_layout(ncol = 3)
fplot
ggsave(fplot, filename = "result/plot/seurat/fplot_1.png", width = 9, height = 6)

fplot_1 <- FeaturePlot(so_qc, reduction = reduction, features = "pMt_RNA") & theme(aspect.ratio = 1)
fplot_2 <- FeaturePlot(so_qc, reduction = reduction, features = "pRp_RNA") & theme(aspect.ratio = 1)
fplot_3 <- FeaturePlot(so_qc, reduction = reduction, features = "pHb_RNA") & theme(aspect.ratio = 1)

options(repr.plot.width = 15, repr.plot.height = 5)
fplot <- fplot_1 + fplot_2 + fplot_3 + plot_layout(ncol = 3)
fplot
ggsave(fplot, filename = "result/plot/seurat/fplot_2.png", width = 9, height = 6)

fplot_1 <- FeaturePlot(so_qc, reduction = reduction, features = "msG0_RNA1") & theme(aspect.ratio = 1)
fplot_2 <- FeaturePlot(so_qc, reduction = reduction, features = "msInt_RNA1") & theme(aspect.ratio = 1)
fplot_3 <- FeaturePlot(so_qc, reduction = reduction, features = "msCC_diff_RNA") & theme(aspect.ratio = 1)

options(repr.plot.width = 15, repr.plot.height = 5)
fplot <- fplot_1 + fplot_2 + fplot_3 + plot_layout(ncol = 3)
fplot
ggsave(fplot, filename = "result/plot/seurat/fplot_3.png", width = 9, height = 6)

Bar plot cluster overlap

cluster_tissue <- ggplot(so_qc@meta.data, aes(x = seurat_clusters, fill = tissue)) + 
  geom_bar(stat = "count", position = "fill") + 
  scale_fill_manual(values = color$tissue) +
  ggtitle("Cluster frequency") + xlab("Cluster") + ylab("Cell frequency") + 
  theme(legend.position = "bottom")

cluster_treatment <- ggplot(so_qc@meta.data, aes(x = seurat_clusters, fill = treatment)) + 
  geom_bar(stat = "count", position = "fill") + 
  scale_fill_manual(values = color$treatment) +
  ggtitle("Cluster frequency") + xlab("Cluster") + ylab("Cell frequency") + 
  theme(legend.position = "bottom")

options(repr.plot.width = 10, repr.plot.height = 5)
cluster_tissue + cluster_treatment + plot_layout(ncol = 2)
ggsave(cluster_tissue, filename = "result/plot/seurat/cluster_tissue.png", width = 6, height = 3)
ggsave(cluster_treatment, filename = "result/plot/seurat/cluster_treatment.png", width = 6, height = 3)

bar_1 <- ggplot(so_qc@meta.data, aes(x = seurat_clusters, y = pHb_RNA)) + geom_boxplot()
bar_2 <- ggplot(so_qc@meta.data, aes(x = seurat_clusters, y = pMt_RNA)) + geom_boxplot()
bar_3 <- ggplot(so_qc@meta.data, aes(x = seurat_clusters, y = pRp_RNA)) + geom_boxplot()

options(repr.plot.width = 10, repr.plot.height = 10)
bar_1 + bar_2 + bar_3 + plot_layout(ncol = 2)

Cluster marker

FACS marker

CD45 - Ptprc for Immune cells Erythroblast marker CD71 Tfrc
CD71 is an integral membrane protein that mediates the uptake of transferrin iron complexes

fplot_1 <- FeaturePlot(so_qc, reduction = reduction, features = "Ptprc") & theme(aspect.ratio = 1) & ggtitle("Ptprc (CD45)")
fplot_2 <- FeaturePlot(so_qc, reduction = reduction, features = "Cd34") & theme(aspect.ratio = 1) & ggtitle("Cd34")
fplot_3 <- FeaturePlot(so_qc, reduction = reduction, features = "Kit") & theme(aspect.ratio = 1) & ggtitle("Kit")
fplot_4 <- FeaturePlot(so_qc, reduction = reduction, features = "Tfrc") & theme(aspect.ratio = 1) & ggtitle("Tfrc (CD71)")
fplot_5 <- FeaturePlot(so_qc, reduction = reduction, features = "Gata2") & theme(aspect.ratio = 1) & ggtitle("Gata2")
fplot_6 <- FeaturePlot(so_qc, reduction = reduction, features = "Gata1") & theme(aspect.ratio = 1) & ggtitle("Gata1")

fplot <- fplot_1 + fplot_2 + fplot_3 + fplot_4 + fplot_5 + fplot_6 + plot_layout(ncol = 3)
options(repr.plot.width = 15, repr.plot.height = 10)
fplot
ggsave(fplot, filename = "result/plot/seurat/fplot_2.png", width = 9, height = 6)

Histocompatibility Class I

H2-K1 - Class I histocompatibility antigen, kappa-B alpha chain
H2-D1 - Class I histocompatibility antigen, D-B alpha chain
H2-L1 - not found

Histocompatibility Class II

H2-Aa - Class II histocompatibility antigen, A-B alpha chain
H2-Ab1 - Class II histocompatibility antigen, A beta chain

H2-Eb1 - Class II histocompatibility antigen, I-E beta chain
H2-Eb2 - Class II histocompatibility antigen, E beta chain

mhcI_genes <- c("H2-K1", "H2-D1")
mhcII_genes <- c("H2-Aa", "H2-Ab1", "H2-Eb1", "H2-Eb2")
cd4_genes <- c("Cd4") 
cd8_genes <- c("Cd8a")

so_qc <- AddModuleScore(so_qc, features = list(mhcI_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msMHCI_RNA")
so_qc <- AddModuleScore(so_qc, features = list(mhcII_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msMHCII_RNA")
so_qc <- AddModuleScore(so_qc, features = list(cd4_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msCd4_RNA")
so_qc <- AddModuleScore(so_qc, features = list(cd8_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msCd8_RNA")

fplot_mhcI <- FeaturePlot(so_qc, reduction = reduction, features = "msMHCI_RNA1") & theme(aspect.ratio = 1) & ggtitle("MHC class I")
fplot_mhcII <- FeaturePlot(so_qc, reduction = reduction, features = "msMHCII_RNA1") & theme(aspect.ratio = 1) & ggtitle("MHC class II")
fplot_cd4 <- FeaturePlot(so_qc, reduction = reduction, features = "msCd4_RNA1") & theme(aspect.ratio = 1) & ggtitle("CD4")
fplot_cd8 <- FeaturePlot(so_qc, reduction = reduction, features = "msCd8_RNA1") & theme(aspect.ratio = 1) & ggtitle("CD8")

fplot_mhc <- fplot_mhcI + fplot_mhcII + fplot_cd4 + fplot_cd8 + plot_layout(ncol = 2)
options(repr.plot.width = 15, repr.plot.height = 10)
fplot_mhc
ggsave(fplot_mhc, filename = "result/plot/seurat/fplot_mhc.png", width = 6, height = 6)

T-cell TCR-CD3 complex

Trac - T cell receptor alpha constant
Trbc1 - T cell receptor beta constant 1
Trbc2 - T cell receptor beta constant 2
Trdc - T cell receptor delta constant
Trgc1 - T cell receptor gamma constant 1
Trgc2 - T cell receptor gamma constant 2

Cd247 - T-cell surface glycoprotein Cd3 zeta chain (Cd3z)
Cd3g - T-cell surface glycoprotein Cd3 gamma chain
Cd3e - T-cell surface glycoprotein Cd3 epsilon chain
Cd3d - T-cell surface glycoprotein Cd3 delta chain

tcr_genes <- c("Trac", "Trbc1", "Trbc2", "Trdc", "Trgc1", "Trgc2")
tcr_cd3_genes <- c("Cd247", "Cd3g", "Cd3e", "Cd3d")

so_qc <- AddModuleScore(so_qc, features = list(tcr_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msTcr_RNA")
so_qc <- AddModuleScore(so_qc, features = list(tcr_cd3_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msTcr_cd3_RNA")

fplot_tcr <- FeaturePlot(so_qc, reduction = reduction, features = "msTcr_RNA1") & theme(aspect.ratio = 1) & ggtitle("TCR")
fplot_tcr_cd3 <- FeaturePlot(so_qc, reduction = reduction, features = "msTcr_cd3_RNA1") & theme(aspect.ratio = 1) & ggtitle("Cd247/Cd3 family")

fplot_tcr_cd3 <- fplot_tcr + fplot_tcr_cd3 + plot_layout(ncol = 2)
options(repr.plot.width = 10, repr.plot.height = 5)
fplot_tcr_cd3
ggsave(fplot_tcr_cd3, filename = "result/plot/seurat/fplot_tcr_cd3.png", width = 6, height = 3)

Warning message:
“The following features are not present in the object: Trgc1, Trgc2, not searching for symbol synonyms”

B-cell Immonogloblin complex

Naive B-cells produce the following Ig classes:
Ighm - Immunoglobulin heavy constant mu (naive B-cells)
Ighd - Immunoglobulin heavy constant delta (naive B-cells)

Through isotope switching the following Ig classes can be produced:
Ighg1 - Immunoglobulin heavy constant gamma (Mouse with Igh1b have Igg2c isotope instead of Igg2a)
Ighg2a - Immunoglobulin heavy constant gamma (NA)
Ighg2b - Immunoglobulin heavy constant gamma
Ighg2c - Immunoglobulin heavy constant gamma
Ighg3 - Immunoglobulin heavy constant gamma
Igha - Immunoglobulin heavy constant alpha
Ighe - Immunoglobulin heavy constant epsilon (NA)

Igkc - Immunoglobulin kappa constant (light chain)
Iglc - Immunoglobulin lambda constant (light chain)

Ighm_genes <- c("Ighm")
Ighd_genes <- c("Ighd")
Ighg_genes <- c("Ighg1", "Ighg2a", "Ighg2b", "Ighg2c", "Ighg3")
Igha_genes <- c("Igha")
Ighe_genes <- c("Ighe") 
Igkc_genes <- c("Igkc")
Iglc_genes <- c("Iglc1", "Iglc2", "Iglc3")

so_qc <- AddModuleScore(so_qc, features = list(Ighm_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msIghm_RNA")
so_qc <- AddModuleScore(so_qc, features = list(Ighd_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msIghd_RNA")
so_qc <- AddModuleScore(so_qc, features = list(Ighg_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msIghg_RNA")
so_qc <- AddModuleScore(so_qc, features = list(Igha_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msIgha_RNA")
#so_qc <- AddModuleScore(so_qc, features = list(Ighe_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msIghe_RNA")
so_qc <- AddModuleScore(so_qc, features = list(Igkc_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msIgkc_RNA")
so_qc <- AddModuleScore(so_qc, features = list(Iglc_genes), assays = "RNA", slot = "data", ctrl = 100, nbin = 25, name = "msIglc_RNA")

fplot_Ighm <- FeaturePlot(so_qc, reduction = reduction, features = "msIghm_RNA1") & theme(aspect.ratio = 1) & ggtitle("Ighm")
fplot_Ighd <- FeaturePlot(so_qc, reduction = reduction, features = "msIghd_RNA1") & theme(aspect.ratio = 1) & ggtitle("Ighd")
fplot_Ighg <- FeaturePlot(so_qc, reduction = reduction, features = "msIghg_RNA1") & theme(aspect.ratio = 1) & ggtitle("Ighg")
fplot_Igha <- FeaturePlot(so_qc, reduction = reduction, features = "msIgha_RNA1") & theme(aspect.ratio = 1) & ggtitle("Igha")
fplot_Igkc <- FeaturePlot(so_qc, reduction = reduction, features = "msIgkc_RNA1") & theme(aspect.ratio = 1) & ggtitle("Igkc")
fplot_Iglc <- FeaturePlot(so_qc, reduction = reduction, features = "msIglc_RNA1") & theme(aspect.ratio = 1) & ggtitle("Iglc")

fplot_ig <- fplot_Ighm + fplot_Ighd + fplot_Ighg + fplot_Igha + fplot_Igkc + fplot_Iglc + plot_layout(ncol = 3)
options(repr.plot.width = 15, repr.plot.height = 10)
fplot_ig
ggsave(fplot_ig, filename = "result/plot/seurat/fplot_ig.png", width = 9, height = 6)

Warning message:
“The following features are not present in the object: Ighg2a, not searching for symbol synonyms”

Save output

# Write tsv file for each sample into 10x sample dir
for (sample_path in unique(so_qc$sample_path)) {
    
    tsv_file <- paste0(sample_path, "/filtered_feature_bc_matrix/barcodes.qc.tsv")
    print(tsv_file)
    cell_id <- so_qc@meta.data[so_qc@meta.data$sample_path == sample_path, ]$cell_id
    
    library(stringr)
    write.table(x = cell_id,
                file = tsv_file,
                row.names = FALSE, 
                col.names = FALSE, 
                sep = "\t", 
                quote = FALSE)

}

[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M1_CpG_Mac_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"
[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M1_CpG_Prog_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"
[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M2_CpG_Mac_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"
[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M2_CpG_Prog_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"
[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M6_control_Mac_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"
[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M6_control_Prog_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"
[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M8_control_Mac_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"
[1] "data/BSA_0355_SM01_10x_SPLENO/OUT/COUNT/M8_control_Prog_transcriptome/filtered_feature_bc_matrix/barcodes.qc.tsv"

# Write Seurat object 
saveRDS(so_qc, so_qc_file)

Its only possible to store two out of three count matrix. Data can be recovered from counts but not vice versa. However, data can not be removed and is prioretized before counts. Therefore we fill counts in the data slot so that it gets exported.

Write Seurat components

# # Meta data 
# write.csv(so_qc@meta.data, "data/object/components/meta_data/seurat_meta.csv")
# # Cluster 
# write.table(so_qc[["rna_snn_res.0.8"]], "data/object/components/cluster/rna_snn_res.0.8.txt", row.names = TRUE, sep = "\t")
# # Variable features
# write.csv(VariableFeatures(so_qc), row.names=FALSE, "data/object/components/variable_features/seurat_variable_features.csv")
# # Count matrix 
# writeMM(GetAssayData(so_qc, slot = "counts") %>% t(), "data/object/components/slots/seurat_counts.mtx")
# write.table(GetAssayData(so_qc, slot = "counts") %>% rownames(), row.names=FALSE, "data/object/components/slots/seurat_counts_genes.csv")
# write.table(GetAssayData(so_qc, slot = "counts") %>% colnames(), row.names=FALSE, "data/object/components/slots/seurat_counts_cellid.csv")
# # Normalized matrix
# writeMM(GetAssayData(so_qc, slot = "data") %>% t(), "data/object/components/slots/seurat_data.mtx")
# write.csv(GetAssayData(so_qc, slot = "data") %>% rownames(), row.names=FALSE, "data/object/components/slots/seurat_data_genes.csv")
# write.csv(GetAssayData(so_qc, slot = "data") %>% colnames(), row.names=FALSE, "data/object/components/slots/seurat_data_cellid.csv")
# # Scaled matrix
# writeMM(GetAssayData(so_qc, slot = "scale.data") %>% as(., "dgCMatrix") %>% t(), "data/object/components/slots/seurat_scale_data.mtx")
# write.csv(GetAssayData(so_qc, slot = "scale.data") %>% rownames(), row.names=FALSE, "data/object/components/slots/seurat_scale_data_genes.csv")
# write.csv(GetAssayData(so_qc, slot = "scale.data") %>% colnames(), row.names=FALSE, "data/object/components/slots/seurat_scale_data_cellid.csv")
# # Reductions 
# write.csv(so_qc@reductions$rna_umap_nno[[]], "data/object/components/reductions/seurat_rna_umap_nno.csv")

Session info

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home/fdeckert/bin/miniconda3/envs/r.4.1.0-FD20200109SPLENO/lib/libopenblasp-r0.3.15.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] stringr_1.4.0               patchwork_1.1.1            
 [3] RColorBrewer_1.1-2          ggplot2_3.3.3              
 [5] Matrix_1.3-4                dplyr_1.0.6                
 [7] celldex_1.2.0               SingleR_1.6.1              
 [9] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
[11] Biobase_2.52.0              GenomicRanges_1.44.0       
[13] GenomeInfoDb_1.28.0         IRanges_2.26.0             
[15] S4Vectors_0.30.0            BiocGenerics_0.38.0        
[17] MatrixGenerics_1.4.0        matrixStats_0.59.0         
[19] SeuratObject_4.0.1          Seurat_4.0.2               

loaded via a namespace (and not attached):
  [1] uuid_0.1-4                    AnnotationHub_3.0.0          
  [3] BiocFileCache_2.0.0           plyr_1.8.6                   
  [5] igraph_1.2.6                  repr_1.1.3                   
  [7] lazyeval_0.2.2                splines_4.1.0                
  [9] BiocParallel_1.26.0           listenv_0.8.0                
 [11] scattermore_0.7               digest_0.6.27                
 [13] htmltools_0.5.1.1             fansi_0.5.0                  
 [15] memoise_2.0.0                 magrittr_2.0.1               
 [17] ScaledMatrix_1.0.0            tensor_1.5                   
 [19] cluster_2.1.2                 ROCR_1.0-11                  
 [21] Biostrings_2.60.0             globals_0.14.0               
 [23] spatstat.sparse_2.0-0         colorspace_2.0-1             
 [25] rappdirs_0.3.3                blob_1.2.1                   
 [27] ggrepel_0.9.1                 crayon_1.4.1                 
 [29] RCurl_1.98-1.3                jsonlite_1.7.2               
 [31] spatstat.data_2.1-0           survival_3.2-11              
 [33] zoo_1.8-9                     glue_1.4.2                   
 [35] polyclip_1.10-0               gtable_0.3.0                 
 [37] zlibbioc_1.38.0               XVector_0.32.0               
 [39] leiden_0.3.8                  DelayedArray_0.18.0          
 [41] BiocSingular_1.8.0            future.apply_1.7.0           
 [43] abind_1.4-5                   scales_1.1.1                 
 [45] DBI_1.1.1                     miniUI_0.1.1.1               
 [47] Rcpp_1.0.6                    viridisLite_0.4.0            
 [49] xtable_1.8-4                  reticulate_1.20              
 [51] spatstat.core_2.1-2           bit_4.0.4                    
 [53] rsvd_1.0.5                    htmlwidgets_1.5.3            
 [55] httr_1.4.2                    ellipsis_0.3.2               
 [57] ica_1.0-2                     farver_2.1.0                 
 [59] pkgconfig_2.0.3               dbplyr_2.1.1                 
 [61] uwot_0.1.10                   deldir_0.2-10                
 [63] utf8_1.2.1                    labeling_0.4.2               
 [65] tidyselect_1.1.1              rlang_0.4.11                 
 [67] reshape2_1.4.4                later_1.2.0                  
 [69] AnnotationDbi_1.54.0          BiocVersion_3.13.1           
 [71] cachem_1.0.5                  munsell_0.5.0                
 [73] tools_4.1.0                   ExperimentHub_2.0.0          
 [75] generics_0.1.0                RSQLite_2.2.5                
 [77] ggridges_0.5.3                evaluate_0.14                
 [79] fastmap_1.1.0                 yaml_2.2.1                   
 [81] goftest_1.2-2                 bit64_4.0.5                  
 [83] fitdistrplus_1.1-5            purrr_0.3.4                  
 [85] RANN_2.6.1                    KEGGREST_1.32.0              
 [87] pbapply_1.4-3                 future_1.21.0                
 [89] nlme_3.1-152                  sparseMatrixStats_1.4.0      
 [91] mime_0.10                     compiler_4.1.0               
 [93] interactiveDisplayBase_1.30.0 filelock_1.0.2               
 [95] curl_4.3.1                    plotly_4.9.3                 
 [97] png_0.1-7                     spatstat.utils_2.1-0         
 [99] tibble_3.1.2                  stringi_1.6.2                
[101] RSpectra_0.16-0               lattice_0.20-44              
[103] IRdisplay_1.0                 vctrs_0.3.8                  
[105] pillar_1.6.1                  lifecycle_1.0.0              
[107] BiocManager_1.30.15           spatstat.geom_2.1-0          
[109] lmtest_0.9-38                 RcppAnnoy_0.0.18             
[111] BiocNeighbors_1.10.0          data.table_1.14.0            
[113] cowplot_1.1.1                 bitops_1.0-7                 
[115] irlba_2.3.3                   httpuv_1.6.1                 
[117] R6_2.5.0                      promises_1.2.0.1             
[119] KernSmooth_2.23-20            gridExtra_2.3                
[121] parallelly_1.25.0             codetools_0.2-18             
[123] MASS_7.3-54                   assertthat_0.2.1             
[125] withr_2.4.2                   sctransform_0.3.2            
[127] GenomeInfoDbData_1.2.6        mgcv_1.8-36                  
[129] grid_4.1.0                    rpart_4.1-15                 
[131] beachmat_2.8.0                IRkernel_1.2                 
[133] tidyr_1.1.3                   DelayedMatrixStats_1.14.0    
[135] Cairo_1.5-12.2                Rtsne_0.15                   
[137] pbdZMQ_0.3-5                  shiny_1.6.0                  
[139] base64enc_0.1-3